Contents
What is the difference between error and accuracy?
Error refers to the disagreement between a measurement and the true or accepted value. You may be amazed to discover that error is not that important in the discussion of experimental results. This statement certainly needs some explanation. As with accuracy, you must know the true or correct value to discuss your error.
What’s the difference between a forecast and an error?
The precision of a forecast gives an idea of the magnitude of the errors but not their overall direction. Of co u rse, as you can see in the figure below, what we want to have is a forecast that is both precise and unbiased. Let’s start by defining the error as the forecast minus the demand.
What is the difference between bias and precision?
The first distinction we have to make is the difference between the precision of a forecast and its bias: Bias represents the historical average error. Basically, will your forecasts be, on average, too high (i.e., you overshot the demand) or too low (i.e., you undershot the demand)? This will give you the overall direction of the error.
What does bias mean in a forecast model?
As a positive error on one item can offset a negative error on another item, a forecast model can achieve very low bias and not be precise at the same time. Obviously, the bias alone won’t be enough to evaluate your forecast precision. But a highly biased forecast is already an indication that something is wrong in the model.
What is the relationship between the accuracy and the loss?
Loss can be seen as a distance between the true values of the problem and the values predicted by the model. Greater the loss is, more huge is the errors you made on the data. Accuracy can be seen as the number of error you made on the data. your situation: a great accuracy but a huge loss, means you made huge errors on a few data.
What are the results of the testing model?
The results of the testing model as the following: First Model: Accuracy: 98.1% Loss: 0.1882 Second Model: Accuracy: 98.5% Loss: 0.0997 Third Model: Accuracy: 99.1% Loss: 0.2544 What is the relationship between the loss and accuracy values?
How are scaled errors used to evaluate forecast accuracy?
Scaled errors were proposed by Hyndman & Koehler (2006) as an alternative to using percentage errors when comparing forecast accuracy across series with different units. They proposed scaling the errors based on the training MAE from a simple forecast method.